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GaussianWorld: Gaussian World Model for Streaming 3D Occupancy Prediction

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arxiv 2412.10373 v1 pith:Z7AS6H33 submitted 2024-12-13 cs.CV cs.AIcs.LG

Sicheng Zuo , Wenzhao Zheng , Yuanhui Huang , Jie Zhou , Jiwen Lu This is my paper
classification cs.CVcs.AIcs.LG
keywords occupancyevolutiongaussianworldcurrentgaussianpredictionscenescenes
verification ladder T0 review T1 audit T2 compute T3 formal
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3D occupancy prediction is important for autonomous driving due to its comprehensive perception of the surroundings. To incorporate sequential inputs, most existing methods fuse representations from previous frames to infer the current 3D occupancy. However, they fail to consider the continuity of driving scenarios and ignore the strong prior provided by the evolution of 3D scenes (e.g., only dynamic objects move). In this paper, we propose a world-model-based framework to exploit the scene evolution for perception. We reformulate 3D occupancy prediction as a 4D occupancy forecasting problem conditioned on the current sensor input. We decompose the scene evolution into three factors: 1) ego motion alignment of static scenes; 2) local movements of dynamic objects; and 3) completion of newly-observed scenes. We then employ a Gaussian world model (GaussianWorld) to explicitly exploit these priors and infer the scene evolution in the 3D Gaussian space considering the current RGB observation. We evaluate the effectiveness of our framework on the widely used nuScenes dataset. Our GaussianWorld improves the performance of the single-frame counterpart by over 2% in mIoU without introducing additional computations. Code: https://github.com/zuosc19/GaussianWorld.

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Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. VISA: VLM-Guided Instance Semantic Auditing for 3D Occupancy World Models

    cs.CV 2026-06 unverdicted novelty 7.0 of 10

    VISA improves closed-set 3D occupancy mIoU on nuScenes by using VLM instance audits as reliability-weighted semantic supervisors during training of existing world models.

  2. S2GO: Streaming Sparse Gaussian Occupancy Prediction

    cs.CV 2025-06 conditional novelty 6.0 of 10

    A sparse-query, streaming Gaussian occupancy predictor achieves state-of-the-art 3D semantic occupancy on nuScenes and KITTI with real-time inference.

  3. GeoDrive: 3D Geometry-Informed Driving World Model with Precise Action Control

    cs.CV 2025-05 conditional novelty 6.0 of 10

    GeoDrive conditions a frozen video diffusion model on a 3D-rendered version of the requested ego trajectory, cutting trajectory-following error by 42% versus Vista while using 99.7% less training data.

  4. A Definition and Roadmap for World Models

    cs.AI 2026-07 conditional novelty 5.0 of 10

    A perspective article defining world models as finite-resource compression of physical state transitions and outlining a roadmap toward physical AGI via unified representations and interactive simulators.

  5. QuadricFormer: Scene as Superquadrics for 3D Semantic Occupancy Prediction

    cs.CV 2025-06 conditional novelty 5.0 of 10

    QuadricFormer represents 3D scenes as a probabilistic mixture of superquadrics, improving accuracy and efficiency over Gaussian-based occupancy prediction on nuScenes.

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